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The primary purpose of this Practice Briefing is to examine the implications of the recommendations of the Royal Institution of Chartered Surveyors (RICS) Review of Investment Valuations to shift towards (explicit) discounted cash flow (DCF) valuations on valuers, clients, risk management and market data.

This Practice Briefing will develop a conceptual framework to articulate the impact of the shift towards explicit modelling assumptions in valuations. A comparative analysis is conducted to contrast traditional valuation approaches with an explicit DCF approach.

The proposed shift to DCF valuations in the real estate industry carries both subtle and profound implications. While on the surface, the move appears to bring minimal change since current valuations are already DCF-based, a deeper exploration reveals a potential transformation to the understanding, analysis and measurement of pricing and risks in commercial real estate.

The traditional techniques adopted in valuations have become pervasive in the industry, hindering more sophisticated modelling and analysis of individual assets and portfolios. Explicitly modelling assumptions in both the initial years of cash flow and perpetuity calculations would unveil fundamental relationships and rationales that have previously been buried within, and perhaps ignored by, an All Risks Yield (ARY).

This briefing identifies the cause of confusion when interpreting ARY differentials on implied target returns and risk and demonstrates how a change to explicit assumptions, such as growth and letting periods, would enhance transparency, enabling more informed client–valuer discussions, which in-turn will boost confidence in valuation accuracy, lead to more sophisticated asset modelling, market data, forecasting and market analysis.

Ordinary direct capitalization is normally considered procyclical in its present form (De Lisle Grissom, 2011); for this reason, an alternative approach to direct capitalization may be useful in the determination of a robust opinion of value. The valuation standards propose an alternative determination of terminal value in the discounted cash flow analysis, recommending that for cyclical assets, the terminal value should consider … “the cyclical nature of the asset and should not be performed in a way that assumes “peak” or “trough” levels of cash flows in perpetuity” (IVS 105 Valuation Approaches and Methods para 50.21 lett e).

The introduction in International Valuation Standards (IVS) of Cyclical Assets raises several questions for the community of real estate professionals and academicians (IVS, 2022, 105 Valuation Approaches and Methods para 50.09 lett d). Cyclical assets can be defined as property whose value is “influenced by upturn and downturn of the market in a significant way” (d’Amato et al., 2019).

The paper proposes different solutions to the problem. The determination of the exit value using cyclical capitalization allows for a prudent assessment of the value and may be used either as a valuation procedure or a risk analysis method.

The valuation comparison with the traditional valuation techniques will be based on an iteration of exit value in order to determine the effects of the valuation procedure on the opinion of value.

The implication of the valuation procedure is the introduction of a countercyclical valuation method to determine the exit value in order to reach stable and reliable valuations for income-producing properties.

These models may have a social implication, providing valuation for income-producing properties that may deal with the property market cycle in a more efficient way, providing efficient valuation for banks and institutions.

The paper is the first application of such a valuation procedure to the determination of exit value.

This study aims to investigate the dynamic interaction between interest rates and commercial property financing offered by Islamic banks in Malaysia.

The authors use the autoregressive distributed lag (ARDL) cointegration methodology to analyse the short- and long-run effect of the interest rates and rental rates on commercial property financing of Islamic banks in Malaysia between 2010: Q1 and 2018: Q2.

The findings reveal that changes in interest rates affect Islamic commercial property financing. This indicates that Islamic banks still rely on interest rates as a benchmark without fully implementing Islamic rental rates. This corroborates the subsequent finding, where overnight policy rates influence commercial property financing.

Despite the authors’ attempt to provide insights into Islamic commercial property financing, the study is limited to secondary data; further research can use survey information to obtain other details that are not included in this study. Similarly, this study does not cover the operation and financial lease debate in Musharakah Mutanaqisah. Future studies can examine the challenges faced by the financial institution towards implementing rental rates in other emerging and developing countries using a different methodology.

This study is the first to investigate the dynamic changes in overnight policy rates, average lending rates and rental rates on Islamic commercial property financing in Malaysia using ARDL techniques. The authors uncover the research and institutional implications of Islamic commercial property financing rates and provide policy and future research directions coupled with the proposed modified rental rate to be developed.

This study aims to investigate the relationship between Airbnb and long-term residential rents, using Santa Monica, California, as a case study. In 2015, Santa Monica adopted the home sharing ordinance (HSO), a stringent regulation aimed at restricting short-term rentals (STR). This research examines the implications of this ordinance on the local housing market.

The synthetic control method (SCM) is applied to a panel data set comprising Airbnb listings and residential rents from multiple cities in Los Angeles County. This approach is used to estimate the causal effects of Santa Monica’s HSO on two outcomes: Airbnb listings and residential rents.

The empirical results show a 60% reduction in Airbnb listings in Santa Monica within two years of implementing the ordinance. Despite this significant decrease, the effect of the regulation on rents was not significant. Suggestive evidence indicates that the ordinance’s ineffectiveness in increasing the number of houses allocated to long-term tenants may have contributed to its negligible impact on rental rates.

To the best of the author’s knowledge, this research is the first to use the SCM for evaluating the impact of STR regulations. It offers crucial insights to policymakers on regulating platforms like Airbnb. The study reveals a scenario where a marked decrease in Airbnb activity did not lower residential rents, highlighting the need for context-specific evaluations in understanding the housing market’s dynamics. Additionally, these findings are valuable for investors considering the implications of regulatory changes in the STR sector.

In this vein, this paper aims to provide empirical evidence on the following questions: Which expansion strategies offer better operational and economic performance? What effects does performance-related diversification have? How do other factors such as size, quality, service offered, location or seasonality interact with performance.

In this paper, the analysis of the effects of growth strategies and hotel attributes on performance is carried out with a sample of 255 hotels that operate internationally. Using panel data and quantile regression, this study evaluates the effect of expansion and diversification on the hotels’ performance.

From these findings, it appears that the equity strategy (own hotels) outperforms non-equity strategies (hotels under rental, franchise and management contract) at the operational level. However, the economic return of the property, both adjusted and unadjusted to risk, is lower under the property ownership strategy than under the franchise and management strategies because, in general, it requires a higher investment. Regarding diversification, the growth strategy based on related diversification in food and beverage services has a negative impact on performance, calling into question the synergies between the two businesses. However, an exception to this effect is seen among those hotels, mainly those in the Caribbean, that opt to provide all-inclusive services, since these hotels achieve better occupancy rates and more stable results.

This study has not taken into account the effect of hotel property revaluation on the performance of the ownership strategy, as there is no information on the historical average revaluation at the level of each individual hotel. This study has also been unable to include information regarding the level of competition and seasonality of sales.

This paper considers a wide number of factors that can influence the performance of hotels. Second, this is the only paper that studies the impact of growth strategies from the point of view of the hotel chain. Also, the sample considered uses data at the individual level on hotels and this research analyses not only operational performance but also economic performance.

This study aims to explore the influence of external factors and the characteristics of young Indian consumers on their behavioral intention toward fashion clothing rental (FCR), using the theory of reasoned action (TRA) as a theoretical framework.

This study used quantitative research methods, collecting data from 396 Indian participants, and tested the proposed hypotheses using PLS-structural equation modeling.

The results indicated that young Indian consumers' favorable attitudes toward FCR are mainly driven by perceived usefulness, novelty-seeking, fashion orientation, narcissism and environmental consciousness. These drivers, together with subjective norms, further lead to their intention to rent fashion clothing. This study also found that perceived risk has a negative impact on consumers' attitudes toward FCR, but minimalism does not significantly affect consumer attitudes.

By integrating additional constructs into traditional TRA, this study contributes to existing literature and provides insight for fashion retailers on the role of consumer characteristics in the adoption of FCR in emerging markets.

This paper aims to examine the impacts of unstable off-farm employment on the probability and stability of farmland rent-out and explore its mechanisms.

The paper adopts Ordinary Least Squares (OLS), Probit, Tobit, Order probit models with two-way fixed effects to conduct empirical analysis based on the balanced panel data collected in 2016 and 2023 with a national representativeness sample of 1,206 rural households in 100 villages across 5 provinces in China.

The empirical results showed that unstable off-farm employment had negative effects on the probability of farmland rent-out, but it had no effects on the stability of farmland rent-out. The mechanism analysis showed that unstable off-farm employment affected the probability of farmland rent-out by decreasing the probability of purchasing houses in city and endowment insurance with high pension. Heterogeneity analysis indicated that the negative effect of unstable off-farm employment was much larger for the households with higher share of labor engaging in off-farm employment outside home county, elder members in the households and those located in the villages of mountain areas.

This paper is the first to define the unstable off-farm employment from the perspective of incontiguous off-farm employment for several years, which could capture the normality rather than particular case in a certain year of off-farm employment among rural labors. Using these new measurements of unstable off-farmland, this paper examined the impacts and mechanisms of share of unstable off-farm employment on the probability and stability of farmland rent-out.

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.

The monetary policy is an important driver of the real estate sector’s performance. The recent wave of monetary tightening in 2022 in response to the cost-of-living crisis has been associated with the decline in housing prices across the globe. There are two main channels through which the US monetary policy may affect the real estate market in the dollar-pegged countries: the cost of serving mortgages (financing cost) and the exchange rate channel (for example, the appreciation of the US dollar and consequently the local currency). The exchange rate channel, which involves the appreciation of the US dollar and the subsequent effect on the local currency, is particularly significant in the case of Dubai, given how international the housing market in Dubai and might be viewed as a tradable good. Using recent data, the purpose of this study to evaluate the spillover impact of the US monetary policy on the housing market performance in the dollar-pegged countries using Dubai as a case study.

For this purpose, this study collected unique longitudinal data on the volume of the monthly transactions of residential properties and performs a panel-data analysis using within-variation models. The changes in the interest rate policy in the USA are determined by the domestic inflation in the USA, thereby, representing an exogenous shock in the UAE.

The results are robust to different specifications and suggest that a strong negative correlation between the interest rate in the USA and the housing sector demand in Dubai. Fiscal policy measures can be taken to mitigate tighter financial conditions in case of policy misalignment.

Few studies have looked at the spillover impact of the global monetary conditions on the real estate market in the GCC region. This study fills this gap by exploring the impact of the US financial conditions on Dubai’s real estate, using panel data analysis.

The purpose of this study is to use a lifetime income measure to evaluate the long-run housing affordability for an understudied cohort of households in the literature – the millennials. The authors do this in the context of Malaysia, measuring long-run affordability for four housing types across geographic locations and income distributions.

This study calculates a long-run housing affordability index (HAI) using data on house prices and household incomes. Essentially a ratio of predicted lifetime incomes to house prices, the HAI is computed for four common housing types in Malaysia from 2005 to 2016 and for six states in the country. The HAI is also compared across four income percentiles.

The analysis reveals varying patterns of housing affordability among different states in Malaysia. Housing affordability has declined since 2010, with most housing types being unaffordable for millennial-led households with the lowest income. Housing is most affordable for those in the highest income bracket, although even here, there are pockets of unaffordable housing as well.

Based on the findings, this study proposes three targeted interventions to improve housing affordability for Malaysian millennials.

This study fills a gap in the literature by examining the long-run housing affordability of Malaysian millennial-led households based on both geographic location and income distribution. The millennial population is understudied in the housing affordability literature, making this study a valuable contribution to the field.